Detecting Actuation of Electrical Devices Using Electrical Noise Over a Power Line

ABSTRACT

Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.

BACKGROUND

The present invention relates to apparatus and methods for detectingelectrical device actuation using electrical noise over a power line.

A common research interest in ubiquitous computing has been thedevelopment of inexpensive and easy-to-deploy sensing systems thatsupport activity detection and context-aware applications in the home.For example, several researchers have explored using arrays of low-costsensors, such as motion detectors or simple contact switches. Suchsensors are discussed by Tapia, E. M., et al. “Activity recognition inthe home setting using simple and ubiquitous sensors,” Proc of PERVASIVE2004, pp. 158-175, Tapia, E. M., et al. “The design of a portable kit ofwireless sensors for naturalistic data collection,” Proc of Pervasive2006, pp. 117-134, and Wilson, D. H., et al. “Simultaneous Tracking andActivity Recognition (STAR) Using Many Anonymous, Binary Sensors,” Procof Pervasive 2005, pp. 62-79, 2005, for example.

Although these solutions are cost-effective on an individual sensorbasis, they are not without some drawbacks. For example, having toinstall and maintain many sensors may be a time-consuming task, and theappearance of many sensors may detract from the aesthetics of the home.This is discussed by Beckmann, C., et al. “Some Assembly Required:Supporting End-User Sensor Installation in Domestic Ubiquitous ComputingEnvironments,” Proc of Ubicomp 2004. pp. 107-124. 2004, and Hirsch, T.,et al. “The ELDer Project: Social, Emotional, and Environmental Factorsin the Design of Eldercare Technologies,” Proc of the ACM Conference onUniversal Usability, pp. 72-79, 2000, for example.

In addition, the large number of sensors required for coverage of anentire home may increase the number of potential failure points. Toaddress these concerns, recent work has focused on sensing throughexisting infrastructure in a home. For example, researchers have lookedat monitoring the plumbing infrastructure in the home to infer basicactivities or using the residential power line to provide indoorlocalization. See, for example, Fogarty, J., et al. “Sensing from theBasement: A Feasibility Study of Unobtrusive and Low-Cost Home ActivityRecognition,” Proc of ACM Symposium on User Interface Software andTechnology (UIST 2006) 2006, and Patel, S. N., et al. “PowerLinePositioning: A Practical Sub-Room-Level Indoor Location System forDomestic Use,” Proceedings of Ubicomp 2006.

Research relating to activity and behavior recognition in a home settingmay be classified by examining the origin of the sensing infrastructuredisclosed herein. The first area of classification includes approachesthat introduce new, independent sensors into the home that directlysense various activities of its residents. This classification includesinfrastructures where a new set of sensors and an associated sensornetwork (wired or wireless) is deployed. A second area encompasses thoseapproaches that take advantage of existing home infrastructure, such asthe plumbing or electrical busses in a home, to sense various activitiesof residents. The goal of the second approach is to lower the adoptionbarrier by reducing the cost and/or complexity of deploying ormaintaining the sensing infrastructure.

Some research approaches use high-fidelity sensing to determineactivity, such as vision or audio systems that capture movement ofpeople in spaces. See, for example, Bian, X., et al. “Using Sound SourceLocalization in a Home Environment,” Proc of the InternationalConference on Pervasive Computing, 2005, and Koile, K., et al. “ActivityZones for Context-Aware Computing,” Proc of UbiComp 2003: UbiquitousComputing, 2003, Seattle, Wash., USA.

Chen et al. in “Bathroom Activity Monitoring Based on Sound,” Proc ofPervasive 2005, pp. 47-61, 2005, installed microphones in a bathroom tosense activities such as showering, toileting, and hand washing. Whilethese approaches may provide rich details about a wide variety ofactivities, they are often very arduous to install and maintain acrossan entire household.

Use of these high fidelity sensors in certain spaces raise concernsabout the balance between value-added services and acceptablesurveillance, particularly in home settings. This is discussed byBeckmann, C., et al. “Some Assembly Required: Supporting End-User SensorInstallation in Domestic Ubiquitous Computing Environments,” Proc ofUbicomp 2004, pp. 107-12A 2004, Hirsch, T., et al. “The ELDer Project:Social, Emotional, and Environmental Factors in the Design of EldercareTechnologies,” Proc of the ACM Conference on Universal Usability, pp.72-79. 2000, and Iachello, G., et al. “Privacy and Proportionality:Adapting Legal Evaluation Techniques to Inform Design In UbiquitousComputing,” Proc of CHI 2005, pp. 91-100, 2005.

Another class of approaches explores the use of a large collection ofsimple, low-cost sensors, such as motion detectors, pressure mats, breakbeam sensors, and contact switches, to determine activity and movement.See Tapia, E. M., et al. “Activity recognition in the home setting usingsimple and ubiquitous sensors,” Proc of PERVASIVE 2004, pp. 158-175,2006, Tapia, E. M., et al. “The design of a portable kit of wirelesssensors for naturalistic data collection,” Proc of Pervasive 2006, pp.117-134, and Wilson, D. H., et al. “Simultaneous Tracking and ActivityRecognition (STAR) Using Many Anonymous, Binary Sensors,” Proc ofPervasive 2005, pp. 62-79, 2005. The Tapia et al. papers discuss homeactivity recognition using many state change sensors, which wereprimarily contact switches. These sensors were affixed to surfaces inthe home and logged specific events for some period of time. Theadvantage of this approach is being able to sense physical activities ina large number of places without the privacy concerns often raised forhigh-fidelity sensing (e.g., bathroom activity). There are also somedisadvantages to this add-on sensor approach, which include therequirements of powering the sensors, providing local storage of loggedevents on the sensor itself, or a wireless communication backbone forreal-time applications. These requirements all complicate the design andmaintenance of the sensors, and the effort to install many sensors andthe potential impact on aesthetics in the living space may alsonegatively impact mass adoption of this solution.

As an example of the often difficult balance of the value of in homesensing and the complexity of the sensing infrastructure, the DigitalFamily Portrait is a peace of mind application for communicatingwell-being information from an elderly person's home to a remotecaregiver. See, for example, Rowan, J. et al. “Digital Family PortraitField Trial: Support for Aging in Place,” Proc of the ACM Conference onHuman Factors in Computing Systems (CHI 2005), pp. 521-530, 2005. Inthis deployment study, movement data was gathered from a collection ofstrain sensors attached to the underside of the first floor of a home.Installation of these sensors was difficult, time-consuming, andrequired access under the floor. Although the value of the applicationwas proven, complexity of the sensing limited the number of homes inwhich the system could be easily deployed.

Other approaches, which are similar to ours, are those that use existinghome infrastructure to detect events. Fogarty et al. “Sensing from theBasement: A Feasibility Study of Unobtrusive and Low-Cost Home ActivityRecognition,” Proc of ACM Symposium on User Interface Software andTechnology (UIST 2006), 2006, explored attaching simple microphones to ahome's plumbing system, thereby leveraging an available homeinfrastructure. The appeal of this solution is that it is low-cost,consists of only a few sensors, and is sufficient for applications, suchas the Digital Family Portrait, for which the monitoring of water usageis a good proxy for activity in the house. This approach requiresrelatively long timescales over which events must be detected, sometimesup to ten seconds. This longer time increases the likelihood ofoverlapping events, which are harder to distinguish.

In contrast, power line event detection operates over timescales ofapproximately half a second and thus overlapping is less likely. Somewater heaters constantly pump hot water through the house, complicatingthe detection of some on-demand activities. Detecting noise on waterpipes introduced by other household infrastructure requires carefulplacement of the microphone sensors. Some homes may not have plumbinginfrastructure that is easily accessible, particularly those with afinished basement or no basement at all. Despite these limitations, thissolution is very complementary to our approach, as some events revealedby water usage, such as turning on a faucet in a sink or flushing atoilet, do not have direct electrical events that could serve aspredictive antecedents. The converse also holds, as a light being turnedon often does not correlate with any water-based activity.

Another “piggybacking” approach is to reuse sensing infrastructure ordevices in the home that may be present for other purposes. For example,ADT Security System's QuietCare offers a peace of mind service thatgathers activity data from the security system's motion detectors. Thisis discussed on the ADT QuietCare website athttp://www.adt.com/quietcare/.

There are other techniques that employ electrical power use to senseactivity. For example, some researchers have monitored electricalcurrent flow to infer the appliances or electrical equipment being usedin the house as a proxy for detecting activity. See, for example,Paradiso, J. A. “Some Novel Applications for Wireless Inertial Sensors,”Proc of NSTI Nanotech 2006, Vol. 3, Boston, Mass., May 7-11, 2006, pp.431-434, and Tapia, E. M., et al. “The design of a portable kit ofwireless sensors for naturalistic data collection,” Proc of Pervasive2006, pp. 117-134.

The Paradiso platform monitors current consumption of various appliancesof interest. Changes in current flow indicate some change in state forthe instrumented appliance, such as a change from on to off. Thissolution requires a current sensor to be installed inline with eachappliance or around its power cord and thus only works well if it issufficient to study the usage of a small subset of appliances and thoseappliances' power feeds are easy accessible. An extension to theParadiso work is to install current sensors on major branch circuits ofthe power lines, but this may require professional installation toprovide an acceptable level of safety. However, it would be desirable todetect a larger number of appliances with less instrumentation and witha much easier deployment phase.

There is therefore a need for apparatus and methods for detectingelectrical device actuation using electrical noise over a power line.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the present invention may be more readilyunderstood with reference to the following detailed description taken inconjunction with the accompanying drawings, wherein like referencenumerals designate like structural elements, and in which:

FIG. 1 illustrates exemplary electrical device actuation detectionapparatus installed in a structure, such as a residence;

FIG. 1 a illustrates prototype electrical device actuation detectionapparatus;

FIG. 1 b illustrates the architecture of an exemplary electrical deviceactuation detection apparatus along with exemplary processing methods;

FIG. 2 a-2 c show sample frequency domain graphs of a light switch thatis toggled at different times;

FIGS. 2 d and 2 e illustrate graphs of frequency versus time and voltageversus frequency associated with an exemplary light switch;

FIG. 3 shows a high-level overview of a simplified model of a home'selectrical infrastructure and where particular noise transfer functionsoccur;

FIG. 4 shows a block diagram of an exemplary power line interfacedevice;

FIG. 5 shows a detailed schematic of an exemplary power line interfacedevice;

FIG. 6 shows a model of a frequency response curve of the power linedata collection module for exemplary 100 Hz-100 kHz and 50 kHz-100 MHzoutputs; and

FIG. 7 illustrates an active embodiment of the exemplary electricaldevice actuation detection apparatus.

DETAILED DESCRIPTION

In order to leverage existing infrastructure to support activitydetection, discussed below is an approach that uses a home's electricalsystem as an information source to observe various electrical events.Detection and classification of these events may be used in a variety ofapplications, such as healthcare, entertainment, home automation, energymonitoring, and post-occupancy research studies. The approach describedherein improves upon previous work of the present inventors relating topower line positioning systems, such as is discussed by Patel, S. N., etal., “PowerLine Positioning: A Practical Sub-Room-Level Indoor LocationSystem for Domestic Use,” Proceedings of Ubicomp 2006, and described inU.S. provisional application Ser. No. 60/817,225, filed Jun. 28, 2006,and U.S. patent application Ser. No. 11/824,204, filed Jun. 8, 2007. Thedisclosed approach uses existing power line infrastructure to providepractical localization within a home. The techniques disclosed hereinpassively sense simple electrical events, whereas the previous worksenses the location of actively tagged objects.

An advantage of the approach is that it requires installation of only asingle, plug-in module (plugged into the existing power lineinfrastructure) that connects to an embedded or personal computer. Thecomputer records and analyzes electrical noise on the power line causedby switching of significant electrical loads. Machine learningtechniques applied to these patterns identify when unique events occur.Examples include human-initiated events, such as turning on or off aspecific light switch or plugging in a CD player, as well as automaticevents, such as a compressor or fan of an HVAC system turning on or offunder control of a thermostat.

By observing actuation of certain electrical devices, the location andactivity of people in the space can be inferred and used forapplications that rely on this contextual information. For example,detecting that a light switch was turned on can be an indication thatsomeone entered a room, and thus an application could adjust thethermostat to make that room more comfortable. Other human-initiatedkitchen events can also be detected, such as a light turning on inside arefrigerator or microwave when its door is opened. These events mayindicate meal preparation.

The disclosed approach also has implications for providing a low-costsolution for monitoring energy usage. An application can log whenparticular electrical loads are active, revealing how and whenelectrical energy is consumed in the household, leading to suggestionson how to maintain a more energy-efficient household. In addition,because the disclosed approach is capable of differentiating between theon and off events of a particular device in real time, those events canbe “linked” to other actuators for a variety of home automationscenarios. The disclosed approach can thus provide for a home automationsystem that maps actuation of a stereo system to an existing lightswitch without having to install additional wiring.

Discussed below is the underlying theory and initial implementationdetails of the disclosed approach to power line event detection. Resultsof a series of tests are discussed that illustrate the stability of thedisclosed approach over time and its ability to sense electrical eventsin different homes. These tests involve installing a device in a singlelocation of a house and collecting data on a variety of electricalevents within that house. Results show that a support vector machinesystem can learn and later classify various unique electrical eventswith accuracies ranging from 85-90%.

Referring to the drawing figures, FIG. 1 illustrates exemplaryelectrical device actuation detection apparatus 10 installed in aresidence 20, for example. The residence 20 has an electrical power line11 (electrical system wiring 11 or power line infrastructure 11) that ismonitored to identify electrical events that occur throughout the home20. It is to be understood that the electrical device actuationdetection apparatus 10 and methods 40 may be used in any structure 20,including homes 20 and businesses 20, for example.

FIG. 1 a illustrates an exemplary reduced-to-practice electrical deviceactuation detection apparatus 10 used for testing purposes. Theexemplary electrical device actuation detection apparatus 10 comprises asingle power line interface module 12, that is plugged via an electricalcord 13 a and plug 13 into an electrical outlet 17 of the existing powerline infrastructure 11 of the home 20. Although not necessarilyrequired, the module 11 may be installed in a convenient, centrallocation in the home 20. The module 12 is connected via a USB interface14, for example, to a computer 15 that includes a software application30 that collects and performs analysis on incoming (detected) electricalnoise present in the power line infrastructure 11. Although not requiredfor normal operation of the electrical device actuation detectionapparatus 10, a USB data acquisition oscilloscope 17 is used in theapparatus 10 to extract a fast Fourier transform (FFT). In operationalapparatus 10, the USB data acquisition oscilloscope 17 is replaced by afast Fourier transform (FFT) circuit, which is preferably embedded inthe power line interface module 12. The software application 30 isconfigured to process the FFT data and learn certain characteristicsrelating to electrical noise produced by switching electrical devices 21(FIG. 1) on or off and is able to predict when those devices 21 areactuated based on the learned phenomena.

The electrical device actuation detection apparatus 10 was developed forcountries that use 60 Hz electrical systems. However, it is to beunderstood that it can easily be extended to frequencies used in othercountries, such as those that use 50 Hz power, for example.

FIG. 1 b illustrates an exemplary architecture of the electrical deviceactuation detection apparatus 10 and exemplary processing methods 40.The exemplary architecture of the electrical device actuation detectionapparatus 10 and the method 40 processes noise present in the power lineinfrastructure 11 resulting from, and associated with, actuation ofelectrical devices 21. The electrical output of the power lineinfrastructure 11 is filtered 31 using the power line interface module12. The filtered electrical output of the power line infrastructure 11is fast Fourier transformed 32 to produce voltage versus frequencyversus time data associated with noise produced when an electricaldevice 21 coupled to the power line infrastructure 11 is toggled(switched) on or off. The fast Fourier transformed noise data is thenprocessed 33 (in the computer 15, for example) to construct a noisesignature associated with the toggling event. The noise signaturecorresponding to the toggling event (toggled electrical switch 21 orload 21) is processed 34 to store it in a database to calibrate theapparatus 10, or find a stored signature in the database that is closestto it to identify which electrical switch 21 or load 21 has beentoggled. The data comprising the noise signature may be remotelytransferred to a central database where it may be processed to providepower consumption analysis.

Theory of Operation

The disclosed approach relies on the fact that abruptly switched(mechanical or solid-state) electrical loads produce broadbandelectrical noise either in the form of a transient or continuous noise.This electrical noise is generated between hot and neutral (known asnormal mode noise), or between neutral and ground (known as common modenoise). Transient and continuous noise on the residential power line 12is typically high in energy and may often be observed with a nearby AMradio. The types of electrical noise that are of interest are producedwithin the home 20 and are created by the fast switching of relativelyhigh currents. For example, a motor-type load, such as a fan, creates atransient noise pulse when it is first turned on and then produces acontinuous noise signal until it is turned off.

In addition, the mechanical switching characteristics of a light switchgenerates transient electrical noise. This is discussed by Howell, E.K., “How Switches Produce Electrical Noise,” IEEE Transactions onElectromagnetic Compatibility, Volume 21:3, pp. 162-170, August 1979.Other examples of noisy events include using a garage door opener,plugging in a power adapter for an electric device, or turning on atelevision. Marubayashi provides a more complete description of thiselectrical noise phenomenon in “Noise Measurements of the ResidentialPower Line,” Proceedings of International Symposium on Power LineCommunications and Its Applications 1997, pp 104-108.

In the case of transient noise, impulses typically last only a fewmicroseconds and include a rich spectrum of frequency components, thatcan range from 10 Hz to 100 kHz. Thus, it is desirable to consider boththe temporal nature (duration) of the transient noise and its frequencycomponents. Depending on the switching mechanism, the loadcharacteristics, and length of transmission line, these impulses can bevery different. For example, FIG. 2 a shows a sample frequency domaingraph of a light switch 21 that is toggled in a home 20 (light onfollowed by light off). The graphs indicate amplitudes at each frequencylevel. Each sample is rich in a broad range of frequencies. On and offevents are each different enough to be distinguished. In addition, theindividual on and off events are similar enough over time to berecognized later. Notice the rich number of high amplitude frequencycomponents for each pulse and their relative strengths. Also, noticethat the signature of a device 21 that is turned on is different fromthe same device when it is turned off. FIG. 2 b shows the same switch 21actuated in the same order as FIG. 2 a, but taken 2 hours later, andFIG. 2 c shows it taken one week later.

The amplitudes of individual frequency components and the duration ofthe impulse produced by each switch 21 are similar between the threegraphs, although there are a few high frequency regions that aredifferent across the samples. Even similar light switches 21 producedifferent signatures, which is likely due to the mechanical constructionof each switch 21 and the influence of the power line length connectedto each switch 21. For example, it was observed that different three-waywall switches 21 connected to the same light each produced discernablesignatures. The main difference was in the relative amplitudes of thefrequencies that are observed. Devices 21 that produce continuous noiseare bounded by transient phenomena, but also exhibit electrical noiseduring their powered operation. For this class of noise, it is possibleto not only identify it based on its transient response but also itscontinuous noise signature.

It is assumed that the noise signature of a particular device 21 dependsboth on the device 21 and the transmission line behavior of theinterconnecting power line 11, so both contributions have been capturedin a single model. FIG. 3 depicts a high-level overview of a simplifiedmodel of a home's electrical infrastructure 11 and where particularnoise transfer functions occur, denoted as H(s). The bottom portion ofFIG. 3 shows three general types of loads found in a home 20, a purelyresistive load, an inductive load where voltage noise is generated froma continuous mechanical switching (motors), and an inductive load wherevoltage noise is generated by an internal oscillator of a solid stateswitch. The transfer functions reflect the fact that both the electricaltransmission line 11 and the data collection module 12 connected to thatline 11 contribute to some transformation of the noise from the source21 to the collection module 12. The observed noise results from theimposition of all the transfer functions against the generated noise.The influence of the transfer function of the transmission line 11 is animportant contributor to the different electrical noise signatures thatwere observed, which indicates why similar device types (e.g., lightswitches 21) can be distinguished and why the location of the datacollection module 12 in the home 20 impacts the observed noise.

In the simplified model, three general classes of electrical noisesources 21 may be found in a home 20, which include resistive loads,inductive loads such as motors, and loads with solid state switching.Purely resistive loads, such as a lamp or an electric stove, do notcreate detectable amounts of electrical noise while in operation,although as a resistor, they produce trace amounts of thermal noise(Johnson noise) at a generally undetectable level. In this particularcase; only transient noise is produced by minute arcing in themechanical switch 21 itself (wall switch 21) when the switch 21 isturned on or off. A motor 21, such as in a fan or a blender, is modeledas both a resistive and inductive load. The continuous breaking andconnecting by the motor brushes creates a voltage noise synchronous tothe AC power of 60 Hz (and at 120 Hz). Solid state switching devices 21,such as MOSFETs found in computer power supplies or TRIACs in dimmerswitches 21 or microwave ovens 21, emit noise that is different betweendevices 21 and is synchronous to an internal oscillator. Thus, thelatter two classes contribute noise from both the external powerswitching mechanism (transient) and the noise generated by the internalswitching mechanism (continuous).

In the United States, the Federal Communications Commission (FCC) setsguidelines on how much electrical noise AC-powered electronic devicescan conduct back onto the power line 11 (Part 15 section of the FCCregulations). Device-generated noise at frequencies between 150 kHz-30MHz cannot exceed certain limits. Regulatory agencies in other countriesset similar guidelines on electronic devices. Although this mainlyapplies to electronic devices, such as those that have solid stateswitching power supplies, this gives us some assurance about the typeand amount of noise we might expect on the power line 11.

It is often extremely difficult to analytically predict the transientnoise from the general description of a load and its switchingmechanism, because ordinary switches are usually not well characterizedduring their make-and-break times. However, it is possible to take amapping approach by learning these observed signatures using supervisedmachine learning techniques. The challenge then becomes finding theimportant features of these transient pulses and determining how todetect the relevant ones of interest.

Hardware Details

To better understand the concept of detecting and learning variouselectrical events in the home 20, a prototype apparatus 10 was builtthat comprises a power line interface module 12 (data collection module12) having three outputs, as illustrated in FIGS. 4 and 5. One outputwas a standard 60 Hz AC power signal, which was used during an initialtesting and exploratory phase. The second output was an attenuated powerline output that was bandpass-filtered with a passband of 100 Hz to 100kHz. The third output was similarly attenuated and was bandpass-filteredwith a 50 kHz to 100 MHz passband. These different filtered outputs werechosen to have the flexibility to experiment with different frequencyranges (FIG. 6). Note that the passbands are not limited to thoseillustrated and discussed herein, and any suitable passbands may beused. FIG. 6 shows a model of the frequency response curve of our powerline data collection apparatus at the 100 Hz-100 kHz and the 50 kHz-100MHz outputs. The 60 Hz dip is from the notch filter.

Both filtered outputs shown in FIG. 6 have a 60 Hz notch filter in frontof their bandpass filters to remove the AC power frequency and enhancethe dynamic range of the sampled data. The interface module 12 was builtso that we could monitor the power line 11 between hot and neutral,neutral and ground, or hot and ground. The noise between hot and neutral(normal mode) was observed because many loads that are desirable toobserve (such as table lamps and small appliances) do not have a groundconnection.

The interface module 12 was connected to one 120V leg or branch of theelectrical system. Most residential homes 20 and apartments in NorthAmerica and many parts of Asia have a single-phase or a splitsingle-phase electrical system. This means there are two 120V electricalbranches coming into the home 20 to supply 240V appliances, but the twobranches are in phase. It was found that the noise generated by devicesof interest connected to the other electrical branch were alreadycoupled to the electrical branch that was interfaced to, and so weredetectable by our system. While this approach was practical andsufficient for a prototype investigation, a coupler may be plugged intoa 240V outlet to ensure direct access to both electrical branches.

Finally, outputs of the power line interface module 12 were connected toa dual-input USB oscilloscope interface 17 (EBest 2000) that hasbuilt-in gain control. Each input has 10-bit resolution with a fullscale voltage of 1V, so the least significant bit represents a voltageof 4 mV. The oscilloscope interface 17 had a real-time sampling rate of100 million samples/sec. A C++ API is provided, resulting in a simplesoftware interface to the sampled signal.

Software Details

The software application 30 used in the prototype apparatus 10 includeda C++ application 30 a that was written to sample the USB oscilloscopeinterface 17 and perform a fast Fourier transform (FFT) on the incomingsignal to separate the component frequencies for analysis. Theapplication 30 a produces a waterfall plot, a commonly used frequencydomain visualization in real-time used for visual inspection (such as isshown in FIG. 2). The application 30 a performs this analysis almost inreal-time, and it has the ability to record the data stream for postprocessing. A second application 30 b, written in Java, performs machinelearning and provides a user interface for the apparatus 10. The Javaapplication 30 b connects via a TCP connection to the FFT application 30a and reads the data values. The Java application 30 b provides the userinterface for surveying the home 20 and remotely accessing the data fromthe power line interface module 12. A Weka toolkit was used as themachine learning implementation. The Weka toolkit is discussed on theWeka website in an article entitled “Weka 3: Data Mining Software inJava,” at http://www.cs.waikato.ac.nz/ml/weka/.

Electrical Events that can be Recognized

Testing was performed to identify electrical devices that could bedetected with the apparatus 10 and see which electrical devices 21 wouldproduce recognizable signatures that can be processed by the machinelearning software 30 b. The apparatus 10 was installed in a single fixedlocation during the data collection process. Data was collected with lowfrequency (100 Hz-100 KHz) and high frequency (50 kHz-100 MHz) ports. Nomajor electrical devices 21 were activated (such as a HVAC,refrigerator, water pumps, etc.) by turning them off for the duration ofthe testing so it was known which devices 21 were causing whichresponse. For each electrical device 21, noise signatures were visuallyobserved and collected while turning a device 21 on, turning it off, andits stable-on state. Table 1 shows the various devices 21 that weretested detected and the events that were observed for each device 21(on, off, continuously on state). Although many more devices, could havebeen observed we only a representative sample of commonly used devicesare shown. Table 1 lists electrical devices 21 that were tested andevents that were detectable. These devices 21 also consistently produceddetectable event signatures.

TABLE 1 On to Off Off to On Device Devices transition transitionContinuously class/type observed noise? noise? on noise? ResistiveIncandescent lights via wall switch Y Y N Microwave door light Y Y NOven light/door Y Y N Electric stove Y Y N Refrigerator door Y Y NElectric oven Y Y N Inductive Bathroom exhaust fan Y Y N (MechanicallyCeiling fan Y Y N Switched) Garage door opener Y Y N Dryer Y Y NDishwasher Y Y N Refrigerator compressor Y Y N HVAC/Heat pump Y Y NGarbage disposal Y Y N Inductive Lights via a dimmer wall switch Y Y Y(Solid State Fluorescent lights via wall switch Y Y N Switched) Laptoppower adapter Y N N Microwave oven Y Y Y Television (CRT, plasma, LCD) YY N

After initial experimentation, it was found that most loads drawing lessthan 0.25 amps were practically undetectable loads relative to prominentelectrical noise (transient and/or continuous). This is relates to thedynamic range of the data collection module 12; a collection module 12with more than 10 bits of resolution would be able to detect lowercurrent devices. The devices 21 listed in Table 1 exhibited strong andconsistently reproducible signatures. However, a limitation was observedrelating to how quickly a given device 21 could be switched (i.e., delaybetween toggles). Depending on the device 21, it was observed thatapproximately 500 ms delay between subsequent toggles was required forthe data collection module 12 to detect a noise impulse successfully.This is largely attributed to the sampling and processing latency fromthe device 21 (e.g., USB latency plus processing delays on the computer15).

While most devices 21 produce a transient pulse only a few microsecondsin duration in their energized state, certain devices 21 continuouslyproduce electrical noise while they are powered, as expected. Forexample, lamp dimmers or wall-mounted dimmer switches produce noise thatwas very rich in harmonics while they were activated. Similarly,microwave ovens couple broadband noise back on the power line during itsuse. These devices 21 tended to produce strong continuous noise above 5kHz and reaching up to 1 MHz. It was also found that switching powersupplies, such as those used in a laptop or personal computer, produceconsiderably higher noise in the 100 kHz-1 MHz range than at the lower100 Hz-5 kHz range.

To understand devices 21 that produce continuous noise, variousswitching power supplies were tested in isolation from other electricalline noise. Using the higher 50 kHz-100 MHz output on the datacollection module 12, it was found that many of these devices 21produced more detectable continuous noise at the higher frequencies. Atthe lower 100 Hz-5 kHz range, fairly low amplitude, continuous noise,and a higher transient noise effect (from the flipping of the switch)was observed.

In the 100 Hz-100 kHz range, motor-based devices 21, such as a ceilingor bathroom exhaust fan, exhibited slightly longer duration transientpluses when activated with a switch, but did not show continuous normalmode noise which would have been expected from repeatedelectromechanical switching from motor brushes. This difference wasattributed to the 60 Hz notch filter, which blocked the 60 Hz powerfrequency. To confirm this, an experiment was conducted in which variousmechanically-switched devices (e.g., fans) were isolated and their noiseoutput was observed. In the case of the fan, the data collection module12 showed the transient pulse, but not the continuous electrical noise.

From these observations, the noise characteristics produced by differentdevices was characterized. It was observed that transient noise producedfrom a single abrupt switching event (e.g., a wall switch) tended toproduce signals rich in high amplitude components in the lower frequencyrange (100 Hz-5 KHz). Inductive loads featuring a solid state switchingmechanism generally produced continuous noise in the 5 kHz-1 MHz range.Inductive loads with mechanically switched voltages produce noise near60 Hz, but the data collection module 12 filtered out much of thatnoise. Thus, analysis of the frequency spectrum may be broken up intotwo parts. The lower frequency space (100 Hz-5 kHz) is effective foranalysis of transient noise events, such as those produced by wallswitches. The higher frequency is better for continuous noise events,such as those produced by TRIACs and switching power supplies. It wasobserved that dim levels can also be gathered from the continuous noisefrequency generated by TRIACs.

Detecting and Learning the Signals

The detection approach requires detection of the transient pulse ofelectrical noise followed by extraction of relevant features forlearning classification.

Detecting Transient Pulses

The filtering hardware in the power line interface module 12 removesmost of the high frequency noise. Some broadband noise is alwayspresent, but typically at low amplitudes. To detect the transientpulses, a simple sliding window algorithm was employed to look fordrastic changes in the input line noise (both beginning and end). Thesedrastic changes, lasting only a few microseconds, are labeled ascandidate signals and are processed further. The sliding windowalgorithm acquires a 1-microsecond sample, which is averaged from dataacquired after performing the FFT on data from the power line interfacemodule 12. Each sample includes frequency components and associatedamplitude values in vector form. Each vector includes amplitude valuesfor frequency intervals ranging between 0 and 50 kHz. The Euclideandistance between the previous vector and the current window's vector isthen computed. When the distance exceeds a predetermined thresholdvalue, the start of a transient is marked. The window continues to slideuntil there is another drastic change in the Euclidean distance (the endof the transient). Although the threshold value was determined throughexperimentation, the thresholds can be readily learned and adapted overtime.

After having isolated the transient, there are N vectors of length L,where N is the pulse width in 1 microsecond increments and L is thenumber of frequency components (2048 for example). A new vector oflength L+1 is then constructed by averaging the corresponding N valuesfor each frequency components. The (L+1)th value is N, the width of thetransient. This value then serves as a feature vector for thatparticular transient.

Learning the Transients

For the learning algorithm, a support vector machine (SVM) was employed,such as is discussed by Burges, C. J. C., “A Tutorial on Support VectorMachines for Pattern Recognition,” Journal of Data Mining and KnowledgeDiscovery, Vol. 2, No. 2, Springer Press, June 1998.

SVMs perform classification by constructing an N-dimensional hyperplanethat optimally separates the data into multiple categories. Theseparation is chosen to have the largest distance from the hyperplane tothe nearest positive and negative examples. Thus, the classification isappropriate for testing data that is near, but not identical, to thetraining data as is the case of the feature vectors for the transients.SVMs are appealing because the feature space is fairly large compared tothe potential training set. Because SVMs employ overfitting protection,which does not necessarily depend upon the number of features, they havethe ability to better handle large feature spaces. The feature vectorsare used as the support vectors in the SVM. The Weka toolkit was used toconstruct an SVM, using labeled training data to later classify thequery points.

Feasibility and Performance Evaluation

To evaluate the feasibility and performance of the disclosed approach,it was tested in six different homes 20 of varying styles, age, sizes,and locations. The transient isolation approach was first tested in asingle home. Next, a feasibility study was conducted in that home 20 fora six-week period to determine the classification accuracy of variouselectrical events over an extended period of time. Finally, for fiveother homes 20, a one-week study was conducted to reproduce the resultsfrom the first home 20.

Transient Isolation Evaluation

To evaluate the feasibility of our automatic transient detectionapproach, data was collected from one home 20 for a four-hour period andhad the software 30 continuously isolate transient signals. During thatperiod, various electrical components were actuated and their timestampswere noted. A total of 100 distinct events were generated during thisperiod. For each event, it was then determined if a transient wasisolated successfully at the noted times. Table 2 shows results of fivedifferent four-hour sessions. The percentage of successfully identifiedtransients out of the number of event triggers are shown in Table 2. Webelieve the reason for the missed events was because of our staticthreshold algorithm. An adaptive threshold approach would mitigate thisproblem.

TABLE 2 Test 1 Test 2 Test 3 Test 4 Test 5 (% found) (% found) (% found)(% found) (% found) 98 93 91 88 96

Classifying Transient Events in Various Homes

The aim of the six-week evaluation was to determine the classificationaccuracy of various types of electrical devices 21 and how often theapparatus 10 needed to be retrained (i.e., signal stability, over time).The other five deployments were used to show that events similar tothose of the initial home 20 could be detected and to show that thetransient noise signatures were temporally stable in other homes 20.Despite the small number of homes 20, a variety of homes and sizes weretested, including older homes with and without recently updatedelectrical systems (see Table). An apartment home 20 in a six-storybuilding was also included, and its electrical infrastructure 11 wassomewhat different from that of a single family home 20. The types ofelectrical devices listed in Table 1 were tested, so it was ensured thatthe homes 20 in which the apparatus 10 was deployed had most of thesedevices 21.

For the entire testing period, the data collection module 12 wasinstalled in the same electrical outlet. For Home 1, data was collectedand labeled at least three times per week during the six-week period.The data collection process involved running the apparatus 10 andtoggling various predetermined electrical devices 21 (see Table 1 forexamples). For each device 21 that was toggled, each on-to-off andoff-to-on event was manually labeled. In addition, at least twoinstances of each event was captured during each session. For Home 1, 41different devices 21 were selected for testing (82 distinct events) andcollected approximately 500 instances during each week. Thus,approximately 3000 labeled samples were collected during the six-weekperiod.

Data was collected and labeled in a similar manner for the shorterone-week deployments. Training data was collected at the beginning ofthe week and additional test data was collected at the end of the week.At least 4 instances of each event were gathered for the training set.Because control of the events was possible, the number of distinctevents were fairly equally distributed among the data and not biasedtowards a single device 21 or switch for all the 6 homes 21.

Table 3 presents descriptions of homes 20 in which the apparatus 10 wasdeployed. Home 1 was used to conduct the long-term six-week deployment.

TABLE 3 Electrical Floors/ Bedrooms/ Deployment Year Remodel Total SizeBathrooms/ Time Home Built Year (Sq Ft)/(Sq M) Style Total Rms. (weeks)1 2003 2003 3/4000/371 1 Family 4/4/13 6 2 2001 2001 3/5000/464 1 Family5/5/17 1 3 1999 1999 1/700/58 1 Bed Apt 1/1/4 1 4 2002 2002 3/2600/241 1Family 3/3/12 1 5 1935 1991 1/1100/102 1 Family 2/1/7 1 6 1967 19811/1500/140 1 Family 2/1/7 1

Tables 4 and 5 show classification accuracies for the different homes wetested. For Home 1, the classification accuracy of test data gathered atvarious times during the six weeks is shown using the training setgathered during the first week. The average overall classificationaccuracy in Home 1 was approximately 85% (Table 4). The accuracy of theclassification for varying training set sizes is also shown. Becausethere can potentially be many events of interest in the home, making thetraining process an arduous task, it was desirable to find the minimumnumber of samples that would provide reasonable performance. The resultssuggest that there is only a slight decrease in classification over thesix-week period. The results also suggest that a small number oftraining instances result in lower classification accuracies.

Table 4 shows performance results of Homes 1. Accuracies are based onthe percentage of correctly identified toggled light switches or otherevents in the test data set. Training happened during week 1, and wereported the accuracies of the classifier for test data from subsequentweeks using that initial training set from week 1. Overallclassification accuracy of a simple majority classifier was 4%.

TABLE 4 Training set Size/Instances Week Week Week Week Week Week perevent 1 (%) 2 (%) 3 (%) 4 (%) 5 (%) 6 (%) 164/2 83 82 81 79 80 79 246/386 84 83 84 82 83 328/4 88 91 87 85 86 86 410/5 90 92 91 87 86 87

Increasing the number of training instances did increase theclassification accuracy. A small number of training samples makes itvery important to have accurate training data. Mislabeling of a singletraining sample can have major impacts on a learned model. For example,the on and off event labels were sometimes flipped for a particularelectrical device 21. This highlights the importance of designing atraining or calibration scheme that mitigates human error during thetraining and labeling process.

The results from the one-week deployment in the five other homes 20 areshown in Table 5, and the test data from the end of the week showedpromising results. No significant differences in accuracy between oldand new homes was observed. The lower classification accuracy for Home 5was the result of low frequency noise that interfered with transientevents.

Table 5 shows performance results of various homes 20. Accuracies arebased on the percentage of correctly identified toggled light switchesor other events in the test data set. The results of a majorityclassifier are also shown. For each home 20, the training of the dataoccurred at the beginning of the week and the test data set was gatheredat the end of that week.

TABLE 5 Distinct Training set Test set Accuracy Majority Home Events(events) (events) (%) classif. (%) 2 32 328 100 87 4 3 48 192 96 88 6 476 304 103 92 3 5 64 256 94 84 3 4 38 152 80 90 8

In the current implementation, the lower frequency spectrum was analyzedwhere solid-state switching devices would produce the lowestinterference from potential continuous noise. Looking at a largerfrequency spectrum provides better classification for certain transientevents.

In addition, the apparatus 10 should detect and to adapt to random noiseevents when looking for transient pulses. The reduced-to-practiceapparatus 10 monitored amplitudes of the component frequencies. Phasedifference between component frequencies, however, may also beconsidered as part of a feature extraction scheme.

Another consideration relates to scaling of the approach. Althoughunlikely in domestic settings, compound events, such as two lightstoggled simultaneously, can produce errors in classification becausetheir combined transient noises produce different feature vectors. Thistype of event is more of a concern in a very large home with manyresidents, or in an apartment building that does not have individuallymetered units. If users regularly flip light switches nearlysimultaneously, this may be trained as a separate event that is distinctfrom those of individual switches.

The reduced-to-practice implementation of the apparatus 10 focuses ondomestic environments, but the apparatus 10 can also be used incommercial settings. Compound events and electrical noise in thesesettings may become a more significant issue. Another issue is that theelectrical lines may be so long that the noise does not reach theanalyzer. Commercial buildings typically have multiple electrical legs,and to mitigate problems with compound events and line distance,multiple line noise analyzers may be installed throughout an officebuilding to isolate the analysis to certain sections of the building. Inorder to scale to the commercial environments, the entire frequency bandmay be used.

The apparatus 10 is more appropriate for detecting and learning fixedelectrical devices as compared to mobile or portable devices. Portabledevices require training the apparatus 10 using any possible outlet thatthe portable device may be plugged into. In addition, plugging theportable device into an extension cord or power strip might produce afingerprint different from one obtained by plugging it directly into anelectrical outlet. With a well-defined set of events that should bedetected, a suitable training plan can readily be devised, although itmay be time-consuming as the set grows larger.

The apparatus 10 does not require deployment of a large number of datacollection modules 12 throughout a home 20. A single data collectionmodule 12 is easier to physically deploy and maintain than a large arrayof distributed sensors. This simplicity of physical installation andmaintenance has its cost in terms of training the machine learningalgorithm 30 to recognize a significant number of electrical loads.

Thus, an approach that provides for a low-cost and easy-to-install powerline event detection apparatus 10 that is capable of identifying certainelectrical events, such as switches that are toggled has been disclosed.This apparatus 10 has implications for applications requiring simpleactivity detection, home automation systems, and energy usageinformation. The apparatus 10 learns and classifies unique electricalevents with high accuracy using standard machine learning techniques.Additionally, deployment of the apparatus 10 in several homes 20 showedlong-term stability and the ability to detect events in a variety ofdifferent types of homes 20. The apparatus 10 has the potential to beintegrated easily into existing applications that aim to provideservices based on detection of various levels of activity.

FIG. 7 illustrates an active embodiment of the electrical deviceactuation detection apparatus 10. This embodiment of the apparatus 10may comprise an interrogator module 10 a and a receiver module 10 b thatare coupled to the power line infrastructure 11. Although illustrated asseparate components, it is to be understood that the interrogator andreceiver modules 10 a, 10 b may be implemented as a singleinterrogator-receiver module 10 c containing transmitter and receiversections. The interrogator and receiver modules 10 a, 10 b, or theinterrogator-receiver module 10 c, are plugged into an electricalreceptacle of the power line infrastructure 11. An electrical device 21,such as an appliance 21, for example, is also coupled to the power lineinfrastructure 11.

In operation, the interrogator module 10 a, or the transmitter portionof the interrogator-receiver module 10 c, sends a known broadbandreference signal, or interrogation signal, over the power lineinfrastructure 11 (up to 9 vpp, for example). The receiver section ofthe interrogator-receiver module 10 c, or the receiver module 10 b,connected to the power line infrastructure 11, listens for the signaland determines if it has changed relative to the reference signal (i.e.,changed signal). Any change in the interrogation or reference signal isthe result of the on-off status of the electrical device 21, orappliance 21, connected to the power line infrastructure 11. The signalvalues corresponding to the on-off status of the electrical device 21are recorded in a database during a calibration process. Theinterrogator module 10 a, or the interrogator-receiver module 10 c, thenlooks for the closest match in the database to identify the associatedelectrical device 21 and its state.

In summary, disclosed are apparatus 10 and methods 40 that identifyactuated electrical devices 21 coupled to an electrical power lineinfrastructure 11 using electrical noise signals generated by thedevices 21. The apparatus 10 is embodied in computer apparatus 12, 15,17 coupled to the electrical power line infrastructure 11 thatidentifies noise signatures associated with electrical switching devices21 and loads 21 that are transmitted over the electrical power lineinfrastructure 11 to determine the on-off status of the one or moreswitching devices 21 and loads 21. The computer apparatus 12, 15, 17comprises software 30 that records noise signatures identifying each ofthe switching devices 21 and loads 21 as they are turned on and off, andidentifies switching devices 21 and loads 21 that are actuated bycomparing a noise signature transmitted over the electrical power lineinfrastructure 11 with the recorded noise signatures.

The electrical noise signals are filtered 31 by a power line interface12 and the filtered electrical noise signals are fast Fouriertransformed 32 to generate voltage versus frequency data associated withan actuated device 21. Thus, electrical noise transmitted over the powerline infrastructure is monitored to detect electrical noise patternsthat correspond to the actuated electrical devices 21. The electricalnoise patterns are processed 33 to construct unique electrical noisesignatures associated with the actuated electrical devices 21. Theunique electrical noise signature may be processed 34 to store it in adatabase, or an electrical noise signature that is stored in thedatabase that corresponds to the generated electrical noise signaturemay be processed 34 to retrieve it from the database, and the signaturesand related data may be displayed. The noise signature data may beremotely transferred to a central database for power consumptionanalysis. The data stored in the database may also be presents in agraphical user interface indicating when and how often an electricaldevice 21 is switched on and off.

In exemplary methods, one or more electrical devices are to anelectrical power line infrastructure by way of one or more switchingdevices. Electrical noise transmitted over the power line infrastructureis monitored. Electrical noise patterns in the monitored electricalnoise are detected that corresponds to actuation of each of the one ormore electrical devices. The detected electrical noise patterns areprocessed to generate a unique electrical signature that identifieswhich of the electrical devices is actuated.

Thus, electrical device actuation detection apparatus and methods havebeen disclosed. It is to be understood that the above-describedembodiments are merely illustrative of some of the many specificembodiments that represent applications of the principles discussedabove. Clearly, numerous and other arrangements can be readily devisedby those skilled in the art without departing from the scope of theinvention.

We claim:
 1. An apparatus configured to detect a change in an electricalstate of one or more electrical devices, the one or more electricaldevices are coupled to an electrical power infrastructure of a structureand generate electrical noise on the electrical power infrastructure,the apparatus comprises: a processing module configured to run on acomputer processor; and a sensing device configured to be removablycoupled to an electrical outlet, the sensing device comprising: a dataacquisition receiver configured to receive the electrical noise via theelectrical outlet when the sensing device is coupled to the electricaloutlet, and further configured to convert the electrical noise into oneor more first data signals, wherein: the electrical outlet iselectrically coupled to the electrical power infrastructure; the sensingdevice is further configured to provide the one or more first datasignals to the processing module; and the processing module is furtherconfigured to determine electrical power consumed by the one or moreelectrical devices at least in part by using the one or more first datasignals.
 2. The apparatus of claim 1, wherein: the electrical noisecomprises one or more electrical pulses on the electrical powerinfrastructure; and the one or more electrical pulses are identifiableon the electrical power infrastructure for a length of time of less thanten microseconds.
 3. The apparatus of claim 2, wherein: the one or moreelectrical pulses are identifiable on the electrical powerinfrastructure for the length of time of less than five microseconds. 4.The apparatus of claim 2, wherein: the electrical noise has a frequencybetween 10 hertz and 100,000 hertz.
 5. A method of detecting andclassifying electrical power usage by one or more electrical devices,the one or more electrical devices are coupled to an electrical powerline of a structure, the method comprising: measuring first electricalnoise on the electrical power line; after measuring the first electricalnoise, using the first electrical noise to determine an occurrence ofone or more electrical events on the electrical power line; and using acomputer processor to associate the occurrence of the one or moreelectrical events with a change in an electrical state of at least onedevice of the one or more electrical devices.
 6. The method of claim 5,wherein: before measuring the first electrical noise, measuring secondelectrical noise on the electrical power line; and before measuring thefirst electrical noise, using the second electrical noise to train thecomputer processor to associate the occurrence of the one or moreelectrical events with the change in the electrical state of at leastone device of the one or more electrical devices.
 7. The method of claim5, wherein: using the computer processor to associate the occurrence ofthe one or more electrical events with the change in the electricalstate comprises: performing one or more transformations to the firstelectrical noise to separate out at least two component frequencies ofthe first electrical noise; and performing a machine learning process tothe at least two component frequencies of the first electrical noise toidentify the change in the electrical state of the at least one deviceof the one or more electrical devices.
 8. The method of claim 5,wherein: using the computer processor to associate the occurrence of theone or more electrical events with the change in the electrical statecomprises: associating the one or more electrical events with aswitching-on or switching-off of the at least one device of the one ormore electrical devices.
 9. The method of claim 5, wherein: using thecomputer processor to associate the occurrence of the one or moreelectrical events with the change in the electrical state comprises:associating the one or more electrical events with the change in theelectrical state from a first state providing a first level ofelectrical power to the at least one device of the one or moreelectrical devices to a second state providing a second level ofelectrical power to the at least one device of the one or moreelectrical devices; and the first level of power is different from thesecond level of power.
 10. The method of claim 5, further comprising:coupling a sensing device to an electrical wall outlet of the structure,wherein: the electrical wall outlet is electrically coupled to theelectrical power line; and measuring the first electrical noisecomprises: measuring the first electrical noise using the sensing devicecoupled to the electrical wall outlet of the structure.
 11. The methodof claim 5, further comprising: before measuring the first electricalnoise, transmitting a first reference signal over the electrical powerline, wherein: the first reference signal interacts with a first deviceof the one or more electrical devices to create the first electricalnoise.
 12. The method of claim 5, wherein: the first electrical noisecomprises one or more electrical pulses on the electrical power line;and the one or more electrical pulses are detectable on the electricalpower line for a length of time of less than ten microseconds.
 13. Themethod of claim 5, wherein: the one or more electrical pulses aredetectable on the electrical power line for the length of time of lessthan five microseconds.
 14. An electrical state change detection deviceconfigured to detect a change in an electrical state of one or moreelectrical devices, the one or more electrical devices are coupled to anelectrical power infrastructure of a structure, the electrical statechange detection device comprises: an interface module comprising: acoupling mechanism configured to removably couple to the electricalpower infrastructure; and a receiver configured to capture electricalnoise on electrical power infrastructure via the coupling mechanism; oneor more filters coupled to the interface module and configured to passone or more portions of the electrical noise; a converter module coupledto the one or more filters and configured to convert the one or moreportions of the electrical noise into one or more data signals; and aprocessing module configured to run on a computer processor, theprocessing module is further configured to identify the change in theelectrical state of the one or more electrical devices at least in partby using the one or more data signals.
 15. The electrical state changedetection device of claim 14, wherein: the processing module comprises:an signature module configured to run on the computer processor andfurther configured to use the one or more data signals to create one ormore noise signatures; and an event classification module configured torun on the computer processor and further configured to use the one ormore noise signatures to determine whether the change in the electricalstate of the one or more electrical devices has occurred and todetermine an event type of the change in the electrical state of the oneor more electrical devices.
 16. The electrical state change detectiondevice of claim 14, wherein: the processing module is configured toidentify when a first one of the one or more electrical devices isturned on or turned off at least in part by using the one or more firstdata signals.
 17. The electrical state change detection device of claim14, wherein: the one or more filters comprise: a 60 hertz filter; and abandpass filter with a passband of: 100 hertz to 100 kilohertz; or 10kilohertz to 100 megahertz.
 18. The electrical state change detectiondevice of claim 14, wherein: the converter module is configured to applya fast Fourier transform to the electrical noise received from the oneor more filters.
 19. The electrical state change detection device ofclaim 14, wherein: the processing module comprises: an event detectionmodule configured to use the one or more data signals to determinewhether one or more electrical events have occurred; a classificationmodule configured to determine the change in the electrical state of theone or more electrical devices at least in party by using the one ormore electrical events; and a training module configured to correlate afirst type of event with a first event of the one or more electricalevents and a second type of event with a second event of the one or moreelectrical events.
 20. The electrical state change detection device ofclaim 14, wherein: the electrical noise comprises one or more electricalpulses on the electrical power infrastructure; and the one or moreelectrical pulses are identifiable on the electrical powerinfrastructure for a length of time of less than ten microseconds.